{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b638e958-0caf-4bf7-a3ea-dd2f5019bf9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "21edd5fc-26c3-4ac8-b0ad-f5084cff8081",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d7bd3b5-e198-4fb1-bbed-ce99f1094783",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2913 entries, 0 to 2912\n",
      "Data columns (total 9 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   标题      2913 non-null   object\n",
      " 1   地区      2913 non-null   object\n",
      " 2   公司名     2913 non-null   object\n",
      " 3   公司领域    2913 non-null   object\n",
      " 4   薪资      2913 non-null   object\n",
      " 5   经验      2913 non-null   object\n",
      " 6   规模      2913 non-null   object\n",
      " 7   福利      2531 non-null   object\n",
      " 8   详情页     2913 non-null   object\n",
      "dtypes: object(9)\n",
      "memory usage: 204.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data_df = pd.read_excel(\"boss岗位.xlsx\")\n",
    "data_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "739fe147-d311-4e3c-a6d0-503066b19611",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>标题</th>\n",
       "      <th>地区</th>\n",
       "      <th>公司名</th>\n",
       "      <th>公司领域</th>\n",
       "      <th>薪资</th>\n",
       "      <th>经验</th>\n",
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       "      <th>福利</th>\n",
       "      <th>详情页</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·公主坟</td>\n",
       "      <td>锐捷网络</td>\n",
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       "      <td>20-30K·13薪</td>\n",
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       "      <td>https://www.zhipin.com/job_detail/42975c4fd5ea...</td>\n",
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       "      <td>数据分析师</td>\n",
       "      <td>上海·浦东新区·张江</td>\n",
       "      <td>中电金信</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>15-25K</td>\n",
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       "      <td>10000人以上</td>\n",
       "      <td>定期体检，节日福利，五险一金，补充医疗保险，带薪年假，零食下午茶</td>\n",
       "      <td>https://www.zhipin.com/job_detail/3f9754994eb4...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·大兴区·亦庄</td>\n",
       "      <td>沃东天骏信息技术</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>30-60K·14薪</td>\n",
       "      <td>5-10年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>加班补助，免费班车，带薪年假，定期体检，补充医疗保险，全勤奖，餐补，年终奖，股票期权，节日福...</td>\n",
       "      <td>https://www.zhipin.com/job_detail/fc118cb2b449...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·马连洼</td>\n",
       "      <td>滴滴</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>15-30K·15薪</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>定期体检，补充医疗保险，五险一金</td>\n",
       "      <td>https://www.zhipin.com/job_detail/805d4a645e38...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·清河</td>\n",
       "      <td>小米</td>\n",
       "      <td>互联网</td>\n",
       "      <td>15-30K·14薪</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>餐补，补充医疗保险，股票期权，年终奖，五险一金，定期体检，节日福利，12%公积金，带薪年假</td>\n",
       "      <td>https://www.zhipin.com/job_detail/6fd7b042d6f7...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "      标题          地区       公司名     公司领域          薪资       经验          规模  \\\n",
       "0  数据分析师  北京·海淀区·公主坟      锐捷网络  通信/网络设备  20-30K·13薪   3-5年本科  1000-9999人   \n",
       "1  数据分析师  上海·浦东新区·张江      中电金信    计算机软件      15-25K   经验不限本科    10000人以上   \n",
       "2  数据分析师   北京·大兴区·亦庄  沃东天骏信息技术     电子商务  30-60K·14薪  5-10年本科    10000人以上   \n",
       "3  数据分析师  北京·海淀区·马连洼        滴滴    移动互联网  15-30K·15薪   3-5年本科  1000-9999人   \n",
       "4  数据分析师   北京·海淀区·清河        小米      互联网  15-30K·14薪   3-5年本科    10000人以上   \n",
       "\n",
       "                                                  福利  \\\n",
       "0  通讯补贴，节日福利，五险一金，定期体检，带薪年假，餐补，包吃，加班补助，员工旅游，零食下午茶...   \n",
       "1                   定期体检，节日福利，五险一金，补充医疗保险，带薪年假，零食下午茶   \n",
       "2  加班补助，免费班车，带薪年假，定期体检，补充医疗保险，全勤奖，餐补，年终奖，股票期权，节日福...   \n",
       "3                                   定期体检，补充医疗保险，五险一金   \n",
       "4      餐补，补充医疗保险，股票期权，年终奖，五险一金，定期体检，节日福利，12%公积金，带薪年假   \n",
       "\n",
       "                                                 详情页  \n",
       "0  https://www.zhipin.com/job_detail/42975c4fd5ea...  \n",
       "1  https://www.zhipin.com/job_detail/3f9754994eb4...  \n",
       "2  https://www.zhipin.com/job_detail/fc118cb2b449...  \n",
       "3  https://www.zhipin.com/job_detail/805d4a645e38...  \n",
       "4  https://www.zhipin.com/job_detail/6fd7b042d6f7...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac22adcd-dadb-4f91-b165-b482ab738e1d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 提取薪资范围并处理缺失值\n",
    "data_df[['薪资下限', '薪资上限']] = data_df['薪资'].str.extract('(\\d+)-(\\d+)K')\n",
    "data_df['薪资下限'] = pd.to_numeric(data_df['薪资下限'], errors='coerce') * 1000  # 转换为数值并处理缺失值\n",
    "data_df['薪资上限'] = pd.to_numeric(data_df['薪资上限'], errors='coerce') * 1000  # 转换为数值并处理缺失值\n",
    "\n",
    "# 计算薪资中间值，跳过缺失值\n",
    "data_df['薪资中间值'] = data_df[['薪资下限', '薪资上限']].mean(axis=1, skipna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7629e732-23db-40ed-a40e-36d14fcd971b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>数据分析师</td>\n",
       "      <td>上海·浦东新区·张江</td>\n",
       "      <td>中电金信</td>\n",
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       "      <td>15-25K</td>\n",
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       "      <td>https://www.zhipin.com/job_detail/3f9754994eb4...</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>20000.0</td>\n",
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       "      <td>数据分析师</td>\n",
       "      <td>北京·大兴区·亦庄</td>\n",
       "      <td>沃东天骏信息技术</td>\n",
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       "      <td>https://www.zhipin.com/job_detail/fc118cb2b449...</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>45000.0</td>\n",
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       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·马连洼</td>\n",
       "      <td>滴滴</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>15-30K·15薪</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>定期体检，补充医疗保险，五险一金</td>\n",
       "      <td>https://www.zhipin.com/job_detail/805d4a645e38...</td>\n",
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       "      <td>22500.0</td>\n",
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       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·清河</td>\n",
       "      <td>小米</td>\n",
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       "      <td>https://www.zhipin.com/job_detail/6fd7b042d6f7...</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>30000.0</td>\n",
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       "      标题          地区       公司名     公司领域          薪资       经验          规模  \\\n",
       "0  数据分析师  北京·海淀区·公主坟      锐捷网络  通信/网络设备  20-30K·13薪   3-5年本科  1000-9999人   \n",
       "1  数据分析师  上海·浦东新区·张江      中电金信    计算机软件      15-25K   经验不限本科    10000人以上   \n",
       "2  数据分析师   北京·大兴区·亦庄  沃东天骏信息技术     电子商务  30-60K·14薪  5-10年本科    10000人以上   \n",
       "3  数据分析师  北京·海淀区·马连洼        滴滴    移动互联网  15-30K·15薪   3-5年本科  1000-9999人   \n",
       "4  数据分析师   北京·海淀区·清河        小米      互联网  15-30K·14薪   3-5年本科    10000人以上   \n",
       "\n",
       "                                                  福利  \\\n",
       "0  通讯补贴，节日福利，五险一金，定期体检，带薪年假，餐补，包吃，加班补助，员工旅游，零食下午茶...   \n",
       "1                   定期体检，节日福利，五险一金，补充医疗保险，带薪年假，零食下午茶   \n",
       "2  加班补助，免费班车，带薪年假，定期体检，补充医疗保险，全勤奖，餐补，年终奖，股票期权，节日福...   \n",
       "3                                   定期体检，补充医疗保险，五险一金   \n",
       "4      餐补，补充医疗保险，股票期权，年终奖，五险一金，定期体检，节日福利，12%公积金，带薪年假   \n",
       "\n",
       "                                                 详情页     薪资下限     薪资上限  \\\n",
       "0  https://www.zhipin.com/job_detail/42975c4fd5ea...  20000.0  30000.0   \n",
       "1  https://www.zhipin.com/job_detail/3f9754994eb4...  15000.0  25000.0   \n",
       "2  https://www.zhipin.com/job_detail/fc118cb2b449...  30000.0  60000.0   \n",
       "3  https://www.zhipin.com/job_detail/805d4a645e38...  15000.0  30000.0   \n",
       "4  https://www.zhipin.com/job_detail/6fd7b042d6f7...  15000.0  30000.0   \n",
       "\n",
       "     薪资中间值  \n",
       "0  25000.0  \n",
       "1  20000.0  \n",
       "2  45000.0  \n",
       "3  22500.0  \n",
       "4  22500.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head()"
   ]
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  {
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   "execution_count": 7,
   "id": "c705f6e5-9edf-4432-a78b-f80aee83b80b",
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·公主坟</td>\n",
       "      <td>锐捷网络</td>\n",
       "      <td>通信/网络设备</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>上海·浦东新区·张江</td>\n",
       "      <td>中电金信</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>经验不限本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·大兴区·亦庄</td>\n",
       "      <td>沃东天骏信息技术</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>5-10年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·马连洼</td>\n",
       "      <td>滴滴</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>22500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·清河</td>\n",
       "      <td>小米</td>\n",
       "      <td>互联网</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>22500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      标题          地区       公司名     公司领域       经验          规模     薪资下限  \\\n",
       "0  数据分析师  北京·海淀区·公主坟      锐捷网络  通信/网络设备   3-5年本科  1000-9999人  20000.0   \n",
       "1  数据分析师  上海·浦东新区·张江      中电金信    计算机软件   经验不限本科    10000人以上  15000.0   \n",
       "2  数据分析师   北京·大兴区·亦庄  沃东天骏信息技术     电子商务  5-10年本科    10000人以上  30000.0   \n",
       "3  数据分析师  北京·海淀区·马连洼        滴滴    移动互联网   3-5年本科  1000-9999人  15000.0   \n",
       "4  数据分析师   北京·海淀区·清河        小米      互联网   3-5年本科    10000人以上  15000.0   \n",
       "\n",
       "      薪资上限    薪资中间值  \n",
       "0  30000.0  25000.0  \n",
       "1  25000.0  20000.0  \n",
       "2  60000.0  45000.0  \n",
       "3  30000.0  22500.0  \n",
       "4  30000.0  22500.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_drop = ['薪资', '福利', '详情页']\n",
    "data_df_cleaned = data_df.drop(columns=columns_to_drop)\n",
    "data_df_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2fbb5e8-8d38-48dd-956b-ab08baefab09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>标题</th>\n",
       "      <th>地区</th>\n",
       "      <th>公司名</th>\n",
       "      <th>公司领域</th>\n",
       "      <th>经验</th>\n",
       "      <th>规模</th>\n",
       "      <th>薪资下限</th>\n",
       "      <th>薪资上限</th>\n",
       "      <th>薪资中间值</th>\n",
       "      <th>地区简称</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·公主坟</td>\n",
       "      <td>锐捷网络</td>\n",
       "      <td>通信/网络设备</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>上海·浦东新区·张江</td>\n",
       "      <td>中电金信</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>经验不限本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·大兴区·亦庄</td>\n",
       "      <td>沃东天骏信息技术</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>5-10年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·马连洼</td>\n",
       "      <td>滴滴</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>22500.0</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·清河</td>\n",
       "      <td>小米</td>\n",
       "      <td>互联网</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>22500.0</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      标题          地区       公司名     公司领域       经验          规模     薪资下限  \\\n",
       "0  数据分析师  北京·海淀区·公主坟      锐捷网络  通信/网络设备   3-5年本科  1000-9999人  20000.0   \n",
       "1  数据分析师  上海·浦东新区·张江      中电金信    计算机软件   经验不限本科    10000人以上  15000.0   \n",
       "2  数据分析师   北京·大兴区·亦庄  沃东天骏信息技术     电子商务  5-10年本科    10000人以上  30000.0   \n",
       "3  数据分析师  北京·海淀区·马连洼        滴滴    移动互联网   3-5年本科  1000-9999人  15000.0   \n",
       "4  数据分析师   北京·海淀区·清河        小米      互联网   3-5年本科    10000人以上  15000.0   \n",
       "\n",
       "      薪资上限    薪资中间值 地区简称  \n",
       "0  30000.0  25000.0   北京  \n",
       "1  25000.0  20000.0   上海  \n",
       "2  60000.0  45000.0   北京  \n",
       "3  30000.0  22500.0   北京  \n",
       "4  30000.0  22500.0   北京  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取地区前两个字\n",
    "data_df_cleaned['地区简称'] = data_df_cleaned['地区'].str[:2]\n",
    "# data_df_cleaned = data_df.drop(columns = data_df['地区'])\n",
    "# 查看清洗后的数据\n",
    "data_df_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "469bedc6-6c86-4269-ba03-ae3472222603",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Filter data for the specified regions\n",
    "regions = ['北京', '广州', '南京', '上海', '成都']\n",
    "filtered_df = data_df_cleaned[data_df_cleaned['地区简称'].isin(regions)]\n",
    "# Visualize the salary distribution by region\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(x='地区简称', y='薪资中间值', data=filtered_df)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('指定地区的薪资中间值分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "364ea3f5-aa8e-4044-be5e-bdc562c08c82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 假设 data_df_cleaned 已经存在并包含所需的数据\n",
    "\n",
    "# 指定要过滤的地区\n",
    "regions = ['北京', '广州', '南京', '上海', '成都']\n",
    "\n",
    "# 过滤数据\n",
    "filtered_df = data_df_cleaned[data_df_cleaned['地区简称'].isin(regions)]\n",
    "\n",
    "# 可视化指定地区的薪资中间值分布\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxenplot(x='地区简称', y='薪资中间值', data=filtered_df)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('指定地区的薪资中间值分布')\n",
    "plt.xlabel('地区')\n",
    "plt.ylabel('薪资中间值')\n",
    "plt.show()\n",
    "\n",
    "# import pandas as pd\n",
    "# from pyecharts.charts import Boxplot\n",
    "# from pyecharts import options as opts\n",
    "# # 筛选指定区域的数据\n",
    "# regions = ['北京', '广州', '南京', '上海', '成都']\n",
    "# filtered_df = data_df_cleaned[data_df_cleaned['地区简称'].isin(regions)]\n",
    "\n",
    "# #为BoxPlot准备数据\n",
    "# data = [filtered_df[filtered_df['地区简称'] == region]['薪资中间值'].tolist() for region in regions]\n",
    "\n",
    "# # 创建 Boxplot\n",
    "# boxplot = Boxplot()\n",
    "# boxplot.add_xaxis(regions)\n",
    "# boxplot.add_yaxis(\"薪资中间值\", boxplot.prepare_data(data))\n",
    "# boxplot.set_global_opts(title_opts=opts.TitleOpts(title=\"指定地区的薪资中间值分布\"))\n",
    "\n",
    "# # 记录chart\n",
    "# boxplot.render(\"salary_distribution.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "49557b87-ef11-4fe3-9986-ce9bec9fbaf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(x='规模', y='薪资中间值', data=data_df_cleaned)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('各公司规模的薪资中间值分布')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e271077-fa58-4e06-beb2-ad444ab2a9fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter data for the specified regions\n",
    "regions = ['北京', '广州', '南京', '上海', '成都']\n",
    "filtered_df = data_df_cleaned[data_df_cleaned['地区简称'].isin(regions)]\n",
    "\n",
    "# Visualize the salary distribution by region\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(x='地区简称', y='薪资中间值', data=filtered_df)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('指定地区的薪资中间值分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e3a8e99a-48e2-45dc-bdef-788035644fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>标题</th>\n",
       "      <th>地区</th>\n",
       "      <th>公司名</th>\n",
       "      <th>公司领域</th>\n",
       "      <th>经验</th>\n",
       "      <th>规模</th>\n",
       "      <th>薪资下限</th>\n",
       "      <th>薪资上限</th>\n",
       "      <th>薪资中间值</th>\n",
       "      <th>地区简称</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·公主坟</td>\n",
       "      <td>锐捷网络</td>\n",
       "      <td>通信/网络设备</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>上海·浦东新区·张江</td>\n",
       "      <td>中电金信</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>经验不限本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·大兴区·亦庄</td>\n",
       "      <td>沃东天骏信息技术</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>5-10年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·马连洼</td>\n",
       "      <td>滴滴</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>22500.0</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·清河</td>\n",
       "      <td>小米</td>\n",
       "      <td>互联网</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>22500.0</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>大数据工程师</td>\n",
       "      <td>惠州·惠城区·仲恺</td>\n",
       "      <td>中软国际</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>3-5年大专</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>惠州</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>大数据开发</td>\n",
       "      <td>南京·江宁区·秣陵</td>\n",
       "      <td>中软国际</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>1-3年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>南京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>大数据开发</td>\n",
       "      <td>南京·江宁区·秣陵</td>\n",
       "      <td>中软国际</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>22500.0</td>\n",
       "      <td>南京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>大数据开发</td>\n",
       "      <td>桂林·临桂区·花生唐</td>\n",
       "      <td>中软国际</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>1-3年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>桂林</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>大数据开发</td>\n",
       "      <td>威海·环翠区·草庙子</td>\n",
       "      <td>中软国际</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>经验不限本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>威海</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2913 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          标题          地区       公司名     公司领域       经验          规模     薪资下限  \\\n",
       "0      数据分析师  北京·海淀区·公主坟      锐捷网络  通信/网络设备   3-5年本科  1000-9999人  20000.0   \n",
       "1      数据分析师  上海·浦东新区·张江      中电金信    计算机软件   经验不限本科    10000人以上  15000.0   \n",
       "2      数据分析师   北京·大兴区·亦庄  沃东天骏信息技术     电子商务  5-10年本科    10000人以上  30000.0   \n",
       "3      数据分析师  北京·海淀区·马连洼        滴滴    移动互联网   3-5年本科  1000-9999人  15000.0   \n",
       "4      数据分析师   北京·海淀区·清河        小米      互联网   3-5年本科    10000人以上  15000.0   \n",
       "...      ...         ...       ...      ...      ...         ...      ...   \n",
       "2908  大数据工程师   惠州·惠城区·仲恺      中软国际    计算机软件   3-5年大专    10000人以上  10000.0   \n",
       "2909   大数据开发   南京·江宁区·秣陵      中软国际    计算机软件   1-3年本科    10000人以上  12000.0   \n",
       "2910   大数据开发   南京·江宁区·秣陵      中软国际    计算机软件   3-5年本科    10000人以上  15000.0   \n",
       "2911   大数据开发  桂林·临桂区·花生唐      中软国际    计算机软件   1-3年本科    10000人以上  15000.0   \n",
       "2912   大数据开发  威海·环翠区·草庙子      中软国际    计算机软件   经验不限本科    10000人以上  10000.0   \n",
       "\n",
       "         薪资上限    薪资中间值 地区简称  \n",
       "0     30000.0  25000.0   北京  \n",
       "1     25000.0  20000.0   上海  \n",
       "2     60000.0  45000.0   北京  \n",
       "3     30000.0  22500.0   北京  \n",
       "4     30000.0  22500.0   北京  \n",
       "...       ...      ...  ...  \n",
       "2908  13000.0  11500.0   惠州  \n",
       "2909  23000.0  17500.0   南京  \n",
       "2910  30000.0  22500.0   南京  \n",
       "2911  20000.0  17500.0   桂林  \n",
       "2912  15000.0  12500.0   威海  \n",
       "\n",
       "[2913 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0afa663c-36d9-4eb3-b201-6a21aac4025d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\胡锦锦\\AppData\\Local\\Temp\\ipykernel_29852\\1992011834.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  data_df_cleaned['需求'].replace(np.nan,'经验不限',inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>标题</th>\n",
       "      <th>地区</th>\n",
       "      <th>公司名</th>\n",
       "      <th>公司领域</th>\n",
       "      <th>经验</th>\n",
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       "      <th>地区简称</th>\n",
       "      <th>需求</th>\n",
       "      <th>学历</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·公主坟</td>\n",
       "      <td>锐捷网络</td>\n",
       "      <td>通信/网络设备</td>\n",
       "      <td>3-5年本科</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>上海·浦东新区·张江</td>\n",
       "      <td>中电金信</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>经验不限本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>上海</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·大兴区·亦庄</td>\n",
       "      <td>沃东天骏信息技术</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>5-10年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>北京</td>\n",
       "      <td>5-10年</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·马连洼</td>\n",
       "      <td>滴滴</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>22500.0</td>\n",
       "      <td>北京</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京·海淀区·清河</td>\n",
       "      <td>小米</td>\n",
       "      <td>互联网</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>22500.0</td>\n",
       "      <td>北京</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>大数据工程师</td>\n",
       "      <td>惠州·惠城区·仲恺</td>\n",
       "      <td>中软国际</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>3-5年大专</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>惠州</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>大专</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>大数据开发</td>\n",
       "      <td>南京·江宁区·秣陵</td>\n",
       "      <td>中软国际</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>1-3年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>南京</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>大数据开发</td>\n",
       "      <td>南京·江宁区·秣陵</td>\n",
       "      <td>中软国际</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>3-5年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>22500.0</td>\n",
       "      <td>南京</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>大数据开发</td>\n",
       "      <td>桂林·临桂区·花生唐</td>\n",
       "      <td>中软国际</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>1-3年本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>桂林</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>大数据开发</td>\n",
       "      <td>威海·环翠区·草庙子</td>\n",
       "      <td>中软国际</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>经验不限本科</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>威海</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2913 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          标题          地区       公司名     公司领域       经验          规模     薪资下限  \\\n",
       "0      数据分析师  北京·海淀区·公主坟      锐捷网络  通信/网络设备   3-5年本科  1000-9999人  20000.0   \n",
       "1      数据分析师  上海·浦东新区·张江      中电金信    计算机软件   经验不限本科    10000人以上  15000.0   \n",
       "2      数据分析师   北京·大兴区·亦庄  沃东天骏信息技术     电子商务  5-10年本科    10000人以上  30000.0   \n",
       "3      数据分析师  北京·海淀区·马连洼        滴滴    移动互联网   3-5年本科  1000-9999人  15000.0   \n",
       "4      数据分析师   北京·海淀区·清河        小米      互联网   3-5年本科    10000人以上  15000.0   \n",
       "...      ...         ...       ...      ...      ...         ...      ...   \n",
       "2908  大数据工程师   惠州·惠城区·仲恺      中软国际    计算机软件   3-5年大专    10000人以上  10000.0   \n",
       "2909   大数据开发   南京·江宁区·秣陵      中软国际    计算机软件   1-3年本科    10000人以上  12000.0   \n",
       "2910   大数据开发   南京·江宁区·秣陵      中软国际    计算机软件   3-5年本科    10000人以上  15000.0   \n",
       "2911   大数据开发  桂林·临桂区·花生唐      中软国际    计算机软件   1-3年本科    10000人以上  15000.0   \n",
       "2912   大数据开发  威海·环翠区·草庙子      中软国际    计算机软件   经验不限本科    10000人以上  10000.0   \n",
       "\n",
       "         薪资上限    薪资中间值 地区简称     需求  学历  \n",
       "0     30000.0  25000.0   北京   3-5年  本科  \n",
       "1     25000.0  20000.0   上海   经验不限  本科  \n",
       "2     60000.0  45000.0   北京  5-10年  本科  \n",
       "3     30000.0  22500.0   北京   3-5年  本科  \n",
       "4     30000.0  22500.0   北京   3-5年  本科  \n",
       "...       ...      ...  ...    ...  ..  \n",
       "2908  13000.0  11500.0   惠州   3-5年  大专  \n",
       "2909  23000.0  17500.0   南京   1-3年  本科  \n",
       "2910  30000.0  22500.0   南京   3-5年  本科  \n",
       "2911  20000.0  17500.0   桂林   1-3年  本科  \n",
       "2912  15000.0  12500.0   威海   经验不限  本科  \n",
       "\n",
       "[2913 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df_cleaned['需求']=data_df_cleaned['经验'].str.extract('^(\\d+/?-\\d+)')+'年'\n",
    "data_df_cleaned['需求'].replace(np.nan,'经验不限',inplace=True)\n",
    "data_df_cleaned['学历']=data_df_cleaned['经验'].str[-2:]\n",
    "data_df_cleaned['需求'].unique()\n",
    "data_df_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "52ebaa84-825b-4f00-897e-194c5d88f3d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\DEV\\\\Pycharm\\\\pythonProject\\\\Shixun\\\\experience_proportion_rose_chart.html'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pyecharts.charts import Pie\n",
    "from pyecharts import options as opts\n",
    "\n",
    "# 清洗经验列的NAN数据\n",
    "data_df = data_df.dropna(subset=['经验'])\n",
    "\n",
    "# 计算经验类的数量\n",
    "experience_counts = data_df['经验'].value_counts().reset_index()\n",
    "experience_counts.columns = ['经验', '数量']\n",
    "\n",
    "# 准备饼图数据\n",
    "data = [list(z) for z in zip(experience_counts['经验'], experience_counts['数量'])]\n",
    "\n",
    "#创建饼图\n",
    "pie = Pie()\n",
    "pie.add(\"\", data, radius=[\"30%\", \"75%\"], rosetype=\"radius\")\n",
    "pie.set_global_opts(title_opts=opts.TitleOpts(title=\"工作经验占比要求\"))\n",
    "pie.set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c} ({d}%)\"))\n",
    "\n",
    "pie.render(\"experience_proportion_rose_chart.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26040e97-a89f-4e28-8bc6-3cedbe4bd93c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4acded3-acb5-49bf-bd03-46561d1abf24",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db218ece-df25-4970-bccf-c0c4a75dcb0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb62fc31-0aa3-4080-be48-16248d1cbd38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c910154a-52a5-4842-a9e2-84fbc7e16178",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99435a4b-d755-4020-912d-5a1459660545",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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